Artificial Intelligence
(Prof. Stuckenschmidt)
Bachelor Seminar
The Chair of Artificial Intelligence typically offers a Bachelor Seminar in the lecture-free time between HWS and FFS. Participation requires the successful participation in the Elective Course “Künstliche Intelligenz” offered in the HWS term. Candidates will be selected based on the grade achieved in this course. Details of the Seminar will be sent to the participants of the course at the end of the HWS term.
Applications for Bachelor and Master Thesis
The Chair of Artificial Intelligence (Prof. Stuckenschmidt) offers the topics for master thesis that can be found here. Applications should be send to Dr. Ines Rehbein (rehbein@uni-mannheim.de). For bachelor thesis we do not offer such a list. Please directly contact the lecturer/
Artificial Intelligence Research Group
We conduct fundamental and applied research in Artificial Intelligence. We develop AI methods that address the specific challenges of a number of application areas in Industry and Society:
People
Faculty
- Prof. Dr. Heiner Stuckenschmidt Artificial Intelligence
- Dr. Christian Meilicke: Knowledge Representation and Reasoning
Researchers
- Keyvan Amiri Elyasi: Predictive Process Mining with Deep Neural Networks
- Alexander Bubak: Data-Driven Inventory Management
- Patrick Betz: Neuro-Symbolic Integration
- Lea Cohausz: Causal Models and Fair ML in Educational Data Mining
- Thilo Dieing: Social Data Science
- Julia Gastinger: Temporal Graph Mining
- Jakob Kappenberger: Social Simulation and Algorithmic Decision Making
- Ricarda Link: Motion-based Human Activity Recognition
- Konrad Özdemir: Temporal Machine Learning
- Darshit Pandya: Activity Monitoring using Automatic Speech Recognition (ASR) systems
PhD Students
- Julian Aßmann (SAP): Downsizing Large Language Models
- Jannik Brinkmann (InES): Mechanistic Interpretation of Neural Networks
- Christoph Huber (HS Mannheim): Visualization of Smart City Data
- Mareike Keil (em: AG) UI-Design for Neurodivergent Users
- Lukas Kirchdorfer (SAP Signavio): Data-driven Business Process Simulation
- Sascha Marton (InES): Gradient-based Learning of Decision Trees and Forests
- Andreas Meyer-Lindenberg (ZI): Digital Mind Twins
- Simon Ott (Austrian Institute of Technology GmbH): Rule-based Learning for Knowledge Graphs
- Sebastian Paull (AIPERIA): Machine Learning for Master Production Planning
- Florian Rupp (HdM Stuttgart): Fair Game Design with Reinforcement Learning
- Andrej Tschalzev (InES): Deep Learning for Tabular Data
- Nils Wilken (InES): Symbolic Goal Recognition
Completed PhDs
- Prof. Dr. Erman Acar (2018): “Knowledge Representation for Automated Decision Making”
- Dr. Taha Alhersh (2021): “From Motion to Human Activity Recognition”
- Dr. Sarah Alturki (2022): “Predicting Student Performance in Interdisciplinary Programs using Methods of Educational Data Mining”.
- Dr. Elena Beisswanger (2013): “Developing Ontological Background Knowledge for Biomedicine”.
- Dr. Fabian Burzlaff (2021): “Knowledge-Driven Architecture Composition”.
- Dr. Alexander Diete (2021): “Towards Multimodal Activity Recognition in Complex Scenarios”
- Dr. Arnab Dutta (2016): “Automated Knowledge Base Extension Using Open Information”.
- Prof. Dr. Kai Eckert (2012): “Usage-driven maintenance of knowledge organization systems”.
- Dr. Daniel Fleischhacker (2016): “Detecting Errors in Linked Data Using Ontology Learning and Outlier Detection”.
- Dr. Oliver Frendo (2021): “Improving Smart Charging for Electric Vehicle Fleets by Integrating Battery and Prediction Models”.
- Dr. David Friede (2023): “Exploring discrete representations in stochastic computation graphs Challenges, benefits, and novel strategies”.
- Dr. Rim Helaoui* (2016): “On Leveraging Statistical and Relational Information for the Representation and Recognition of Complex Human Activities”.
- Dr. Jakob Huber* (2019): “Data-driven Decision Support for Perishable Goods”.
- Dr. Jonathan Kobbe (2023): “Automatic generation of structured explanations for arguments from consequences”.
- Dr. Elena Kuss (2019): “Evaluation of Process Model Matching Techniques”.
- Dr. Christian Meilicke* (2011): “Alignment Incoherence in Ontology Matching”.
- Dr. Jan Noessner* (2014): “Efficient Maximum A-Posteriori Inference in Markov Logic and Application in Description Logics”.
- Dr. Andreas Nolle (2021): “Federated Knowledge Base Debugging in DL-LiteA".
- Dr. Michael Oesterle (2024) “Self-learning restriction-based governance of multi-agent systems”.
- Dr. Christoph Pinkel (2016): “Incremental, Interactive,Inter-Model Mapping Generation”.
- Dr. Bernhard Schäfer* (2023): “Recognizing Hand-drawn Diagrams in Images”
- Dr. Anne Schlicht* (2012): “Scaling Up Description Logic Reasoning by Distributed Resolution”.
- Dr. Jörg Schönfisch (2018): “Scalable Handling of Uncertain Data and Knowledge Graphs”
- Dr. Diana Sola (2023): “Recommending activities for business process model”.
- Dr. Timo Sztyler* (2019): “Sensor-based human activity recognition: Overcoming issues in a real world setting”
- Dr. Christoph Theil (2022): “Uncertainty, Risk, and Financial Disclosures -
Applications of Natural Language Processing in Behavioral Economics”. - Dr. Caecilia Zirn (2016): “Fine-grained Position Analysis for Political Texts”.
* With Distinction
Former Members still active in Science
- Erman Acar – Assistant Professor in Explainable AI for Finance – University of Amsterdam
- Sarah Alturki – Assistant Professor Princess Nourah Bint Abdulrahman University, Rihyad, Saudi Arabia
- Christian Bartelt – Professor for Methods and Applications of Machine Learning at TU Clasuthal.
- Melisachew Wudage Chekol – Assistant Professor for Data Management at the Utrecht University
- Kai Eckert – Professor at University of Applied Science Mannheim
- Ioana Hulpus – Assistant Professor at Utrecht University
- Rim Helaoui – Director Data & AI Innovation & Strategy at Philips Research Eindhoven
- Elena Kuss – Professor for Business Informatics Reuthlingen University of Applied Science
- Goran Glavas – Professor for Natural Language Processing at Würzburg University
- Stefan Lüdtke – Tenure Track Assistant Professor for Marine Data Science at University of Rostock
- Federico Nanni - Research Data Scientist at the Allan Turing Institute London
- Mathias Niepert – Professor for Machine Learning and Simulation at Stuttgart University
- Sanja Stajner – Senior Research Scientist at Symanto.
- Timo Sztyler – Research Scientist at NEC Labs Europe Heidelberg
Projects
Projects
- NEST-bw: Netzwerk zu Verfahren der Studienorientierung und Selbstreflexion (2024 – )
- Meeting KI: Entwicklung eines intelligenten Meeting- Unterstützungsystems (2024 – 2026)
- TransforMA: Technologie- und Wissenstransfer für die aktive Gestaltung von Transformationsprozessen (2023 – 2027)
- sMArt roots – Smart City Modellstadt Mannheim (2021 – 2026)
- CAIUS: Consequences of AI Applications on Urban Societies (2019 – 2025)
- KISync – AI for integrated supply chain optimization (2022 – 2025)
Software and Data
- Activity Recognition Data and Algorithms
- AnyBURL (A state of the art rule learner for Knowledge-Base Completion)
- ALCOMO (a tool for repairing ontology alignments)
- Rockit (a query engine for Markov Logic)
- ELOG (a reasoner for log-linear description logics.
Courses FSS
Industrial Applications of Artificial Intelligence – Lecture (Lecture, english)
Kurstyp:
Lecture
ECTS:
6
Kurs geeignet für:
Kurssprache:
english
SWS 1:
2
Teilnahme:
Präsenz/Online live & aufgezeichnet
Lernziel:
Expertise:
Students will acquire knowledge about possible applications of machine learning in different branches of industry as well as the dominant methods used in these areas:
Methodological competence:
Successful participants will be able to: Identify potential for applying AI methods in different areas of industry; Decide on a suitable method for addressing typical problems in these industries
Personal competence:
Participants will learn to reflect and document their own learning process
Students will acquire knowledge about possible applications of machine learning in different branches of industry as well as the dominant methods used in these areas:
- Primary Sector: Agriculture, Energy Production
- Secondary Sector: Production, Supply Chain Management
- Tertiary Sector: Healthcare, Education, Finance
Methodological competence:
Successful participants will be able to: Identify potential for applying AI methods in different areas of industry; Decide on a suitable method for addressing typical problems in these industries
Personal competence:
Participants will learn to reflect and document their own learning process
Empfohlene Voraussetzungen:
Literatur:
Various Scientific Publications – details in the lecture slides
Prüfungsleistung:
Submission of a Learning Portfolio
Lektor(en):
Prof. Dr. Heiner Stuckenschmidt
Beschreibung:
Participants will learn about the use of Artificial Intelligence methods, mostly from the field of machine learning in different sectors and industries. They will learn about application areas in the primary, secondary and tertiary sector, get an introduction to examples of such applications that have been published on a scientific level and gather some experience in working with data from the respective fields using publically available datasets.
Weitere Informationen
1 SWS geben die Dauer eines Kurses an, der wöchentlich während eines Semesters angeboten wird. Eine SWS entspricht 45 Minuten.
Wirtschaftsinformatik II: Grundlagen der Modellierung (Lecture, german)
Kurstyp:
Lecture
ECTS:
6.0
Kurs geeignet für:
Bachelor, Master
Kurssprache:
german
SWS 1:
2
Teilnahme:
Präsenz/Online live
Lernziel:
Fachkompetenz:
Methodenkompetenz:
Personale Kompetenz:
- Kenntnisse aktueller Modellierungssprachen und Werkzeugen.
- Verständnis für Grundprinzipien und Formalen Grundlagen der Modellierung von Anwendungsdomänen und Prozessen.
Methodenkompetenz:
- Beschreibung von Domänen und Prozesse einfacher und mittlerer Komplexität mit Hilfe gängiger Sprachen und Werkzeuge
Personale Kompetenz:
- Verständnis komplexer Zusammenhänge, Arbeiten im Team, Kommunikation von Modellierungsentscheidungen
Empfohlene Voraussetzungen:
Prüfungsleistung:
Studienbeginn ab HWS 2011:
Erfolgreiche Teilnahme am Übungsbetrieb
Schriftliche Klausur (90 Minuten)
Erfolgreiche Teilnahme am Übungsbetrieb
Schriftliche Klausur (90 Minuten)
Studienbeginn vor HWS 2011:
Schriftliche Klausur (90 Minuten)
Lektor(en):
Prof. Dr. Heiner Stuckenschmidt, Dr. Christian Meilicke
Beschreibung:
Die Vorlesung behandelt die Rolle konzeptueller Modellierung in der Wirtschaftsinformatik. Es werden Vorteile und Grenzen der Modlelierung im Unternehmenkontext aufgezeigt und Modellierungssprachen und Werkzeuge eingeführt. Inhalte der Veranstaltung umfassen unter anderem:
- Modellierungsprinzipien
- Praxisnahe Sprachen (UML, BPMN)
- Formale Grundlagen von Modellierungssprachen (Logik, Pertri-Netze)
- Modellierungswerkzeuge.
Weitere Informationen
1 SWS geben die Dauer eines Kurses an, der wöchentlich während eines Semesters angeboten wird. Eine SWS entspricht 45 Minuten.
Courses HWS
Data Science in Action (ENGAGE.EU Signature Course) (Lecture, english)
Kurstyp:
Lecture
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
english
SWS 1:
2
Teilnahme:
Online live
Empfohlene Voraussetzungen:
Literatur:
Recommended Papers from invited speakers
Prüfungsleistung:
Written Essay
Lektor(en):
Prof. Dr. Heiner Stuckenschmidt
Beschreibung:
The Mannheim Center for Data Science (MCDS) offers a lecture series on “Data Science in Action” together with the European University ENGAGE.EU (Signature Course). Renowned researchers from the University of Mannheim and its partner universities Université Toulouse Capitole, Tilburg University, Hanken School of Economics, Norwegian School of Economics (NHH) and WU Vienna University of Economics and Business will provide insights into their data-based research. The speakers represent various disciplines, including business administration, computer science, political science, business education, media and communication studies, sociology, psychology and linguistics. The lecture series thus represents the relevance of data science in its entire breadth for science and society.
Weitere Informationen
1 SWS geben die Dauer eines Kurses an, der wöchentlich während eines Semesters angeboten wird. Eine SWS entspricht 45 Minuten.
Decision Support (Lecture, english)
Kurstyp:
Lecture
ECTS:
6.0
Kurs geeignet für:
Bachelor, Master
Kurssprache:
english
SWS 1:
2
Teilnahme:
Präsenz live
Lernziel:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Empfohlene Voraussetzungen:
Prüfungsleistung:
Written examination (90 minutes), homework assignments, case studies
Lektor(en):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Beschreibung:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
Weitere Informationen
1 SWS geben die Dauer eines Kurses an, der wöchentlich während eines Semesters angeboten wird. Eine SWS entspricht 45 Minuten.
Decision Support (Lecture, english)
Kurstyp:
Lecture
ECTS:
6.0
Kurs geeignet für:
Bachelor, Master
Kurssprache:
english
SWS 1:
1
Teilnahme:
Präsenz live
Lernziel:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Empfohlene Voraussetzungen:
Prüfungsleistung:
Written examination (90 minutes), homework assignments, case studies
Lektor(en):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Beschreibung:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
Weitere Informationen
1 SWS geben die Dauer eines Kurses an, der wöchentlich während eines Semesters angeboten wird. Eine SWS entspricht 45 Minuten.
Decision Support (Lecture, english)
Kurstyp:
Lecture
ECTS:
6.0
Kurs geeignet für:
Bachelor, Master
Kurssprache:
english
SWS 1:
2
Teilnahme:
Präsenz live
Lernziel:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Empfohlene Voraussetzungen:
Prüfungsleistung:
Written examination (90 minutes), homework assignments, case studies
Lektor(en):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Beschreibung:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
Weitere Informationen
1 SWS geben die Dauer eines Kurses an, der wöchentlich während eines Semesters angeboten wird. Eine SWS entspricht 45 Minuten.
Artificial Intelligence (Lecture, german)
Kurstyp:
Lecture
ECTS:
6.0
Kurs geeignet für:
Bachelor
Kurssprache:
german
SWS 1:
4
Teilnahme:
Präsenz live
Lernziel:
Fachkompetenz:
Ziele und Grundlagen der Künstlichen Intelligenz. Suchverfahren als universelle Problemlösungsverfahren. Problemkomplexität und Heuristische Lösungen. Eigenschaften und Zusammenhang zwischen unterschiedlichen Suchverfahren.
Methodenkompetenz:
Beschreibung konkreter Aufgaben als Such-, Constraint- oder Planungsproblem. Implementierung unterschiedlicher Suchverfahren und Heuristiken.
Ziele und Grundlagen der Künstlichen Intelligenz. Suchverfahren als universelle Problemlösungsverfahren. Problemkomplexität und Heuristische Lösungen. Eigenschaften und Zusammenhang zwischen unterschiedlichen Suchverfahren.
Methodenkompetenz:
Beschreibung konkreter Aufgaben als Such-, Constraint- oder Planungsproblem. Implementierung unterschiedlicher Suchverfahren und Heuristiken.
Empfohlene Voraussetzungen:
Prüfungsleistung:
Erfolgreiche Teilnahme am Übungsbetrieb
schriftliche Klausur (90 Minuten)
schriftliche Klausur (90 Minuten)
Lektor(en):
Dr. Christian Meilicke
Beschreibung:
- Problemeigenschaften und Problemtypen
- Problemlösen als Suche, Anwendung im Bereich Computerspiele
- Constraintprobleme und deren Lösung
- Logische Constraints
Weitere Informationen
1 SWS geben die Dauer eines Kurses an, der wöchentlich während eines Semesters angeboten wird. Eine SWS entspricht 45 Minuten.
Publications (past 5 years only)
2025
- Kappenberger, J., Stuckenschmidt, H. und Gerdon, F. (2025). Pricing parking for fairness — A simulation study based on an empirically calibrated model of parking behavior. Transportation Research. Part A, Policy and Practice, 193, 1–29.
- Kirchdorfer, L., Blümel, R., Kampik, T., der Aa, H. und Stuckenschmidt, H. (2025). Discovering multi-agent systems for resource-centric business process simulation. Process Science, 2, 1–26.
- Paull, S., Bubak, A. und Stuckenschmidt, H. (2025). Machine learning for master production scheduling: combining probabilistic forecasting with stochastic optimisation. Expert Systems with Applications, 271, 1–13.
2024
- Amiri Elyasi, K., Sola, D., Meilicke, C., der Aa, H. und Stuckenschmidt, H. (2024). Knowledge graph completion for activity recommendation in business process modeling. Künstliche Intelligenz : KI, 1–15.
- Bubak, A., Rolf, B., Reggelin, T., Lang, S. und Stuckenschmidt, H. (2024). An LSTM network-based genetic algorithm for integrated procurement and scheduling optimisation. International Journal of Production Research, 1–30.
- Cohausz, L., Tschalzev, A., Bartelt, C. und Stuckenschmidt, H. (2024). Investigating demographic features and their connection to performance, predictions, and fairness in EDM models. Journal of Educational Data Mining, 16, 177–213.
- Marton, S., Lüdtke, S., Bartelt, C., Tschalzev, A. und Stuckenschmidt, H. (2024). Explaining neural networks without access to training data. Machine Learning, 113, 3633-3652.
- Meilicke, C., Chekol, M. W., Betz, P., Fink, M. und Stuckenschmidt, H. (2024). Anytime bottom-up rule learning for large-scale knowledge graph completion. The VLDB Journal, 33, 131–161.
- Rink, J., Szabo, K., Hoyer, C., Saver, J. L., Nour, M., Audebert, H. J., Kunz, W. G., Froelich, M. F., Heinzl, A., Tschalzev, A., Hoffmann, J., Schoenberg, S. O. und Tollens, F. (2024). Mobile stroke units services in Germany: A cost-effectiveness modeling perspective on catchment zones, operating modes, and staffing. European Journal of Neurology, 1–9.
- Rink, J., Tollens, F., Tschalzev, A., Bartelt, C., Heinzl, A., Hoffmann, J., Schoenberg, S. O., Marzina, A., Sandikci, V., Wiegand, C., Hoyer, C. und Szabo, K. (2024). Establishing an MSU service in a medium-sized German urban area — clinical and economic considerations. Frontiers in Neurolgy, 15, 1–9.
- Spreitzenbarth, J., Bode, C. und Stuckenschmidt, H. (2024). Artificial intelligence and machine learning in purchasing and supply management: A mixed-methods review of the state-of-the-art in literature and practice. Journal of Purchasing and Supply Management, 30, 1–21.
2023
- Amaefule, C. O., Lüdtke, S., Klostermann, A., Hinz, C. A., Kampa, I., Kirste, T. und Teipel, S. (2023). At crossroads in a virtual city: Effect of spatial disorientation on gait variability and psychophysiological response among healthy older adults. Gerontology, 69, 450–463.
- Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2023). Correction to: Data-driven prototyping via natural- language-based GUI retrieval. Automated Software Engineering, 30, 1–2.
- Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2023). Data-driven prototyping via natural-language-based GUI retrieval. Automated Software Engineering, 30, 1–34.
- Schäfer, B., der Aa, H., Leopold, H. und Stuckenschmidt, H. (2023). Sketch2Process: End-to-end BPMN Sketch Recognition Based on Neural Networks. IEEE Transactions on Software Engineering, 49, 2621-2641.
- Spreitzenbarth, J., Bode, C. und Stuckenschmidt, H. (2023). Designing an AI purchasing requisition bundling generator. Computers in Industry, 155, Article 104043, 1–14.
- Umlauft, J., Johnson, C. W., Roux, P., Trugman, D. T., Lecointre, A., Walpersdorf, A., Nanni, U., Gimbert, F., Rouet-Leduc, B., Hulbert, C., Lüdtke, S., Marton, S. und Johnson, P. A. (2023). Mapping glacier basal sliding applying machine learning. Journal of geophysical research : JGR. F, Earth surface, 128, 1–20.
2022
- Alturki, S., Cohausz, L. und Stuckenschmidt, H. (2022). Predicting master’s students’ academic performance: An empirical study in Germany. Smart Learning Environments, 9, 1–22.
- Alturki, S., Hulpus, I. und Stuckenschmidt, H. (2022). Predicting academic outcomes: A survey from 2007 till 2018. Technology, Knowledge and Learning, 27, 275–307.
- Alturki, S. und Stuckenschmidt, H. (2022). Assessing students' self-assessment ability in an interdisciplinary domain. Journal of Applied Research in Higher Education : JARHE, 14, 1449-1466.
- Burzlaff, F., Wilken, N., Bartelt, C. und Stuckenschmidt, H. (2022). Semantic interoperability methods for smart service systems: A survey. IEEE Transactions on Engineering Management : EM, 69, 4052-4066.
- Cohausz, L. (2022). When probabilities are not enough – A framework for causal explanations of student success models. Journal of Educational Data Mining, 14, 52–75.
- Marton, S., Lüdtke, S. und Bartelt, C. (2022). Explanations for neural networks by neural networks. Applied Sciences, 12, 1–14.
- Niemann, F., Lüdtke, S., Bartelt, C. und Ten Hompel, M. (2022). Context-aware human activity recognition in industrial processes. Sensors, 22, 1–14.
- Sola, D., der Aa, H., Meilicke, C. und Stuckenschmidt, H. (2022). Exploiting Label Semantics For Rule-Based Activity Recommendation In Business Process Modeling. Information Systems : IS, 108.
- Teipel, S., Amaefule, C. O., Lüdtke, S., Görß, D., Faraza, S., Bruhn, S. und Kirste, T. (2022). Prediction of disorientation by accelerometric and gait features in young and older adults navigating in a virtually enriched environment. Frontiers in Psychology, 13, 1–14.
2021
- Alhersh, T., Stuckenschmidt, H., Rehman, A. U. und Brahim Belhaouari, S. (2021). Learning human activity from visual data using deep learning. IEEE Access, 9, 106245-106253.
- Alturki, S. und Alturki, N. (2021). Using educational data mining to predict students' academic performance for applying early interventions. Journal of Information Technology Education : JITE. Innovations in Practice : IIP, 20, 121–137.
- Civitarese, G., Sztyler, T., Riboni, D., Bettini, C. und Stuckenschmidt, H. (2021). POLARIS: Probabilistic and ontological activity recognition in smart-homes. IEEE Transactions on Knowledge and Data Engineering : TKDE, 33, 209–223.
- Frendo, O., Gaertner, N. und Stuckenschmidt, H. (2021). Open source algorithm for smart charging of electric vehicle fleets. IEEE Transactions on Industrial Informatics, 17, 6014-6022.
- Frendo, O., Gärtner, N. und Stuckenschmidt, H. (2021). Improving smart charging prioritization by predicting electric vehicle departure time. IEEE Transactions on Intelligent Transportation Systems, 22, 6646-6653.
- Huber, J. und Stuckenschmidt, H. (2021). Intraday shelf replenishment decision support for perishable goods. International Journal of Production Economics, 231, 1–14.
- Schäfer, B., Keuper, M. und Stuckenschmidt, H. (2021). Arrow R-CNN for handwritten diagram recognition. International Journal on Document Analysis and Recognition : IJDAR, 24, 3–17.
2020
- Becker, M., Hulpus, I., Opitz, J., Paul, D., Kobbe, J., Stuckenschmidt, H. und Frank, A. (2020). Explaining arguments with background knowledge : Towards knowledge-based argumentation analysis. Datenbank-Spektrum, 20, 131–141.
- Frendo, O., Graf, J., Gärtner, N. und Stuckenschmidt, H. (2020). Data-driven smart charging for heterogeneous electric vehicle fleets. Energy and AI, 1, 1–13.
- Huber, J. und Stuckenschmidt, H. (2020). Daily retail demand forecasting using machine learning with emphasis on calendric special days. International Journal of Forecasting, 36, 1420-1438.
- Theil, C. K., Štajner, S. und Stuckenschmidt, H. (2020). Explaining financial uncertainty through specialized word embeddings.
ACM/
IMS Transactions on Data Science : TDS, 1, Article 6, 1–19.
2024
- Amiri Elyasi, K., van der Aa, H. und Stuckenschmidt, H. (2024). PGTNet: a process graph transformer network for remaining time prediction of business process instances. In , Advanced information systems egineering : 36th international conference, CAiSE 2024, Limassol, Cyprus, June 3–7, 2024 : proceedings (S. 124–140). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Betz, P., Galarraga, L., Ott, S., Meilicke, C., Suchanek, F. M. und Stuckenschmidt, H. (2024). PyClause – Simple and efficient rule handling for knowledge graphs. In , Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Demo Track : Jeju, 03–09 August 2024 (S. 1–3). tba, International Joint Conferences on Artificial Intelligence: Jeju, South Korea.
- Betz, P., Lüdtke, S., Meilicke, C. und Stuckenschmidt, H. (2024). Rule Confidence Aggregation for Knowledge Graph Completion. In , Rules and reasoning : 8th International Joint Conferrence, RuleML+RR 2024, Rucharest, Romania, September 16–18, 2024, Proceedings (S. 1–1). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Cohausz, L., Kappenberger, J. und Stuckenschmidt, H. (2024). Combining fairness and causal graphs to advance both. In , Fairness and Bias in AI : Proceedings of the 2nd Workshop on Fairness and Bias in AI, co-located with 27th European Conference on Artificial Intelligence (ECAI 2024) (S. 1–14). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Cohausz, L., Kappenberger, J. und Stuckenschmidt, H. (2024). What fairness metrics can really tell you: A case study in the educational domain. In , LAK'24: Proceedings of the 14th Learning Analytics and Knowledge Conference (S. 792–799). , Association for Computing Machinery: Kyoto, Japan.
- Dieing, T. I., Scheffler, M. und Cohausz, L. (2024). Enhancing chatbot-assisted study program orientation. In , Workshopband der 22. Fachtagung Bildungstechnologien (DELFI) : 09.09.-11.09.2024, Fulda, Deutschland (S. 223–232). , Gesellschaft für Informatik (GI): Bonn.
- Gastinger, J., Meilicke, C., Errica, F., Sztyler, T., Schuelke, A. und Stuckenschmidt, H. (2024). History repeats itself: A Baseline for Temporal Knowledge Graph Forecasting. In , Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Demo Track : Jeju, 03–09 August 2024 (S. ). , International Joint Conferences on Artificial Intelligence: Jeju, South Korea.
- Karvonen, A., Wright, B., Rager, C., Angell, R., Brinkmann, J., Smith, L. R., Verdun, C. M., Bau, D. und Marks, S. (2024). Measuring progress in dictionary learning for language model interpretability with board game models. In , ICML 2024 Workshop on Mechanistic Interpretability (S. 1–17). , OpenReview: .
- Keil, M. (2024). AI-supported UI design for enhanced development of neurodiverse-friendly IT-systems. In , NordDesign 2024. Proceedings (S. 1–10). , The Design Society: .
- Kirchdorfer, L., Blümel, R., Kampik, T., van der Aa, H. und Stuckenschmidt, H. (2024). AgentSimulator: an agent-based approach for data-driven business process simulation. In , 2024 6th International Conference on Process Mining : ICPM 2024 : Kgs. Lyngby, Denmark, 14–18 October 2024 : proceedings (S. 97–104). , IEEE: Piscataway, NJ.
- Klerings, A., Bartelt, C. und Mueller, A. (2024). Developmentally plausible multimodal language models are highly modular. In , The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning : proceedings of the Second BabyLM Challenge : November 15–16, 2024 (S. 118–139). , Association for Computational Linguistics: Miami, FL, USA.
- Kolthoff, K., Bartelt, C., Ponzetto, S. P. und Schneider, K. (2024). Self-elicitation of requirements with automated GUI prototyping.
In , Proceedings of the 39th IEEE/
ACM International Conference on Automated Software Engineering : ASE 2024 : October 28 – November 1, 2024, Sacramento, California, USA (S. 2354-2357). , IEEE/ ACM: Sacramento, CA, USA. - Kolthoff, K., Kretzer, F., Bartelt, C., Maedche, A. und Ponzetto, S. P. (2024). Interlinking user stories and GUI prototyping: A semi-automatic LLM-based approach. In , 2024 IEEE 32nd International Requirements Engineering Conference (RE) (S. 1–9). , IEEE: Reykjavik, Iceland.
- Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). GRANDE: Gradient-Based Decision Tree Ensembles for tabular data. In , International Conference on Learning Representations (S. 1–27). , OpenReview.net: .
- Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). GradTree: Learning axis-aligned decision trees with gradient descent. In , Proceedings of the 38th AAAI Conference on Artificial Intelligence (S. 14323-14331). , AAAI Press: Washington, DC.
- Oesterle, M., Grams, T. und Bartelt, C. (2024). DRAMA at the PettingZoo: Dynamically restricted action spaces for multi-agent reinforcement learning frameworks. In , Proceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024, Hilton Hawaiian Village Waikiki Beach Resort, Hawaii, USA, January 3–6, 2024 (S. 7810-7819). , Department of IT-Management, Shidler College of Business, University of Hawaii: Honolulu, HI.
- Oesterle, M., Grams, T., Bartelt, C. und Stuckenschmidt, H. (2024). RAISE the bar: Restriction of action spaces for improved social welfare and equity in traffic management. In , Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (S. 1492-1500). , International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC.
- Ott, S., Meilicke, C. und Stuckenschmidt, H. (2024). Reevaluation of inductive link prediction. In , Rules and reasoning : 8th international joint conference, RuleML+RR 2024, Bucharest, Romania, September 16–18, 2024 : proceedings (S. 75–90). Lecture notes in computer science, Springer: Cham.
- Pandya, D. und Stuckenschmidt, H. (2024). Evaluating the impact of different acoustic contexts on German speech recognition. In , 2024 IEEE international conference on pervasive computing and communications workshops and other affiliated events (PerCom workshops) : 11–15 March 2024 in Biarritz, France (S. 1–5). , IEEE: Piscataway, NJ.
- Rana, A., Oesterle, M. und Brinkmann, J. (2024). GOV-REK: Governed Reward Engineering Kernels for designing robust multi-agent reinforcement learning systems. In , AAMAS '24: proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (S. 2429-2431). , International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC.
- Scheffler, M., Dieing, T. I. und Cohausz, L. (2024). Developing a personalized study program recommender. In , Workshopband der 22. Fachtagung Bildungstechnologien (DELFI) : 9.-11. September 2024, Fulda, Deutschland (S. 233–240). , Gesellschaft für Informatik (GI): Bonn.
- Tschalzev, A., Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). A data-centric perspective on evaluating machine learning models for tabular data. In , The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (S. 1–35). , NeurIPS: Vancouver, BC.
- Tschalzev, A., Nitschke, P., Kirchdorfer, L., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). Enabling mixed effects neural networks for diverse, clustered data using Monte Carlo methods. In , Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence: Jeju, 03–09 August 2024 (S. ). , International Joint Conferences on Artificial Intelligence: Jeju, South Korea.
- Wilken, N., Cohausz, L., Bartelt, C. und Stuckenschmidt, H. (2024). Fact Probability Vector Based Goal Recognition. In , 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) (S. 4254-4261). Frontiers in Artificial Intelligence and Applications, IOS Press: Amsterdam [u. a.].
2023
- Brinkmann, J., Swoboda, P. und Bartelt, C. (2023). A multidimensional analysis of social biases in vision transformers.
In , Proceedings of the IEEE/
CVF International Conference on Computer Vision (ICCV) (S. 4914-4923). , IEEE: Paris, France. - Busch, K., Rochlitzer, A., Sola, D. und Leopold, H. (2023). Just tell me: Prompt engineering in business process management. In , Enterprise, business-process and information systems modeling : 24th International Conference, BPMDS 2023, and 28th International Conference, EMMSAD 2023, Zaragoza, Spain, June 12–13, 2023, proceedings (S. 3–11). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u. a.].
- Cohausz, L., Tschalzev, A., Bartelt, C. und Stuckenschmidt, H. (2023). Investigating the importance of demographic features for EDM-predictions. In , Proceedings of the 16th International Conference on Educational Data Mining (S. 125–136). , International Educational Data Mining Society: Bengaluru, India.
- Ernst, J. S., Marton, S., Brinkmann, J., Vellasques, E., Foucard, D., Kraemer, M. und Lambert, M. (2023). Bias mitigation for large language models using adversarial learning. In , Proceedings of the 1st Workshop on Fairness and Bias in AI co-located with 26th European Conference on Artificial Intelligence (ECAI 2023),Kraków, Poland, October 1st, 2023 (S. 1–14). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Friede, D., Reimers, C., Stuckenschmidt, H. und Niepert, M. (2023). Learning disentangled discrete representations. In , Machine learning and knowledge discovery in databases : research track : European conference, EMCL PKDD 2023, Turin, Italy, september 18–22,2023, proceedings, part IV (S. 593–609). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Gastinger, J., Sztyler, T., Sharma, L., Schuelke, A. und Stuckenschmidt, H. (2023). Comparing apples and oranges? : on the evaluation of methods for temporal knowledge graph forecasting. In , Machine learning and knowledge discovery in databases: research track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023 : proceedings. Part III (S. 533–549). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Gautschi, T., Gschwend, T., Oberländer, L., Ponzetto, S. P. und Stuckenschmidt, H. (2023). Der Mannheim Master in Social Data Science. In , INFORMATIK 2023 : Designing Futures: Zukünfte gestalten (S. 81–88). GI-Edition : Lecture Notes in Informatics. Proceedings, Gesellschaft für Informatik (GI): Bonn.
- He, Y., Schreckenberger, C., Stuckenschmidt, H. und Wu, X. (2023). Towards utilitarian online learning – A review of online algorithms in open feature space. In , Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (S. 6647-6655). , International Joint Conferences on Artificial Intelligence Organization: Macao SAR.
- Keil, M. V. und Bleisinger, O. (2023). Benutzeroberflächen von MBSE-Tools und deren Auswirkung auf neurodivergente Systemarchitekten. In , Tag des Systems Engineering (S. 9–15). , Books on Demand GmbH: Bremen.
- Kobbe, J., Hulpus, I. und Stuckenschmidt, H. (2023). Effect graph: Effect relation extraction for explanation generation. In , Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE) (S. 116–127). , Association for Computational Linguistics: Toronto, Canada.
- Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2023). Outlying aspect mining via sum-product networks. In , Advances in knowledge discovery and data mining: 27th Pacific-Asia Conference on knowledge discovery and data mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023 : proceedings. Part I (S. 27–38). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2023). GradTree: Learning axis-aligned decision trees with gradient descent. In , (S. 1–17). , Neural Information Processing Systems Foundation, Inc. (NeurIPS): New Orleans.
- Oesterle, M. und Sharon, G. (2023). Socially optimal non-discriminatory restrictions for continuous-action games. In , Proceedings of the 37th AAAI Conference of Artificial Intelligence. Vol. 10 (S. 11638-11646). , AAAI Press: Washington, DC.
- Oesterle, M. und Sharon, G. (2023). Socially optimal non-discriminatory restrictions for continuous-action games. In , KI 2023: Advances in Artificial Intelligence : 46th German Conference on AI, Berlin, Germany, September 26–29, 2023, Proceedings (S. 252–256). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Ott, S., Betz, P., Stepanova, D., Gad-Elrab, M. H., Meilicke, C. und Stuckenschmidt, H. (2023). Rule-based knowledge graph completion with canonical models. In , CIKM'23: Proceedings of the 32nd ACM International Conference on Information & Knowledge Management (S. 1971-1981). , ACM: Birmingham, United Kingdom.
- Popko, M., Bader, S., Lüdtke, S. und Kirste, T. (2023). Discovering behavioural predispositions in data to improve human activity recognition. In , iWOAR '22: Proceedings of the 7th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence : September 19–20, 2022, Rostock, Germany (S. 1–7). , Association for Computing Machinery: New York, NY, USA.
- Schreckenberger, C., He, Y., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2023). Online random feature forests for learning in varying feature spaces. In , Proceedings of the 37th AAAI Conference on Artificial Intelligence. Vol. 4 (S. 4587-4595). , AAAI Press: Washington, DC.
- Sola, D., der Aa, H., Meilicke, C. und Stuckenschmidt, H. (2023). Activity recommendation for business process modeling with pre-trained language models. In , The Semantic Web : 20th International Conference, ESWC 2023, Hersonissos, Crete, Greeece, May 28-June 1, 2023. Proceddings (S. 316–334). , Springer International Publishing: Cham, Switzerland.
- Sola, D., Warmuth, C., Schäfer, B., Badakhshan, P., Rehse, J.-R. und Kampik, T. (2023). SAP Signavio Academic Models: A large process model dataset. In , Process Mining Workshops : ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23–28, 2022, Revised Selected Papers (S. 453–465). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u. a.].
- Spreitzenbarth, J., Bode, C. und Stuckenschmidt, H. (2023). Designing an AI-enabled bundling generator in an automotive case study. In , Proceedings of the 56th Annual Hawaii International Conference on System Sciences (S. 4495-4504). , University of Hawaii at Manoa: Honolulu, HI.
- Steinmann, L., Nowotka, D., Oberländer, L., Pfuhl, H., Stuckenschmidt, H. und Drechsler, R. (2023). Workshop: Aktuelle Entwicklungen und Perspektiven (an Hochschulen) im Bereich Data Science. In , Informatik 2023 : designing futures: Zukünfte gestalten ; 26. – 29. September 2023, Berlin (S. 73–80). GI-Edition : Lecture Notes in Informatics. Proceedings, Ges. für Informatik: Bonn.
- Theil, K., Hovy, D. und Stuckenschmidt, H. (2023). Top-down influence? Predicting CEO personality and risk impact from speech transcripts. In , Proceedings of the seventeenth International AAAI Conference on web and social media : June 5th – 8th 2023, Limassol, Cyprus (S. 832–841). International AAAI Conference on Web and Social Media, AAAI Press: Palo Alto, Calif..
- Wilken, N., Cohausz, L., Bartelt, C. und Stuckenschmidt, H. (2023). Planning landmark based goal recognition revisited: Does using initial state landmarks make sense? In , KI 2023: Advances in Artificial Intelligence : 46th Conference on AI, Berlin, Germany, September 26–29, 20023, proceedings (S. 231–244). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
2022
- Betz, P., Meilicke, C. und Stuckenschmidt, H. (2022). Adversarial explanations for knowledge graph embeddings. In , Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence : Vienna, 23–29 July 2022 (S. 2820-2826). , International Joint Conferences on Artificial Intelligence: Darmstadt ; Vienna.
- Betz, P., Meilicke, C. und Stuckenschmidt, H. (2022). Supervised knowledge aggregation for knowledge graph completion. In , The semantic web: 19th International Conference, ESWC 2022, Hersonissos, Crete, Greece, May 29-June 2, 2022 : proceedings (S. 74–92). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Cohausz, L. (2022). Towards real interpretability of student success prediction combining methods of XAI and social science. In , Proceedings of the 15th International Conference on Educational Data Mining (S. 361–367). , International Educational Data Mining Society: Durham.
- Cohausz, L., Wilken, N. und Stuckenschmidt, H. (2022). Plan-similarity based heuristics for goal recognition. In , 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) : PerCom Workshops 2022 (S. 316–321). , IEEE: Pisa.
- Friede, D. und Niepert, M. (2022). Efficient learning of discrete-continuous computation graphs. In , 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6–14 December 2021 (S. 6720-6732). Advances in Neural Information Processing Systems, Curran Associates: Red Hook, NY.
- Kappenberger, J., Theil, K. und Stuckenschmidt, H. (2022). Evaluating the impact of AI-based priced parking with social simulation. In , Social Informatics: 13th International Conference, SocInfo 2022, Glasgow, UK, October 19–21, 2022, proceedings (S. 54–75). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2022). Exchangeability-aware sum-product networks. In , Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, 23–29 July 2022 (S. 4864-4870). , International Joint Conferences on Artificial Intelligence Organization: Wien.
- Oesterle, M., Bartelt, C., Lüdtke, S. und Stuckenschmidt, H. (2022). Self-learning governance of black-box multi-agent systems. In , Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XV : International Workshop, COINE 2022, virtual event, May 9, 2022, revised selected papers (S. 73–91). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2022). Dynamic forest for learning from data streams with varying feature spaces. In , Cooperative information systems : 28th International Conference, CoopIS 2022, Bozen-Bolzano, Italy, October 4–7, 2022, Proceedings (S. 95–111). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Sola, D., Meilicke, C., der Aa, H. und Stuckenschmidt, H. (2022). On the Use of Knowledge Graph Completion Methods for Activity Recommendation in Business Process Modeling. In , Business Process Management Workshops : BPM 2021 international workshops, Rome, Italy, September 6–10, 2021, revised selected papers (S. 5–17). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u. a.].
- Wilken, N., Cohausz, L., Schaum, J., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2022). Leveraging planning landmarks for hybrid online goal recognition. In , International Conference on Automated Planning and Scheduling ICAPS (2022) : June 13–17, 2022, virtual (S. ). , CEUR Workshop Proceedings: Aachen.
2021
- Betz, P., Niepert, M., Minervini, P. und Stuckenschmidt, H. (2021). Backpropagating through Markov Logic Networks. In , Proceedings of 15th International Workshop on Neural-Symbolic Learning and Reasoning as part of the 1st International Joint Conference on Learning & Reasoning (IJCLR 2021) Virtual conference, October 25–27, 2021. (S. ). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Burzlaff, F. und Bartelt, C. (2021). Knowledge-driven architecture composition: Assisting the system integrator to reuse integration knowledge. In , ICWE 2021 : 21st International Conference on Web Engineering, Biarritz, France, May 18–21, 2021 ; proceedings (S. 305–319). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Hoffmann, L., Bartelt, C. und Stuckenschmidt, H. (2021). Knowledge injection via ML-based initialization of neural networks. In , Proceedings of the CIKM 2021 Workshops (CIKMW 2021) co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021) : Gold Coast, Queensland, Australia, November 1–5,2021 (S. 1–6). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Meilicke, C., Betz, P. und Stuckenschmidt, H. (2021). Why a naive way to combine symbolic and latent knowledge base completion works surprisingly well. In , 3rd Conference on Automated Knowledge Base Construction (S. 1–26). , OpenReview.net: Online.
- Metzger, N., Hoffmann, L., Bartelt, C., Stuckenschmidt, H., Wommer, M. und Bescos del Castillo, M. B. (2021). Towards trace-graphs for data-driven test case mining in the domain of automated driving. In , Third IEEE International Conference on Artificial Intelligence Testing: AITest 2021 : proceedings : 23–26 August 2021, online event (S. 41–48). , IEEE: Piscataway, NJ.
- Nolte, F., Wilken, N. und Bartelt, C. (2021). Rendezvous delivery: Utilizing autonomous electric vehicles to improve the efficiency of last mile parcel delivery in urban areas. In , 2021 IEEE PerCom Workshops : PerAwareCity 2021, 6th IEEE Workshop on Pervasive Context-Aware Smart Cities and Intelligent Transportation Systems, March 22–26, 2021 in Kassel, Germany (S. 148–153). , IEEE Computer Society: Piscataway, NJ.
- Pernpeintner, M. (2021). Self-learning governance of competitive multi-agent systems. In , Organic Computing : Doctoral Dissertation Colloquium 2020 (S. 47–63). Intelligent Embedded Systems, Kassel University Press: Kassel.
- Pernpeintner, M. (2021). Toward a self-learning governance loop for competitive multi-attribute MAS. In , AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems : Richland, SC, Virtual Event United Kingdom, May, 2021 (S. 1619-1621). , International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC.
- Pernpeintner, M., Bartelt, C. und Stuckenschmidt, H. (2021). Governing black-box agents in competitive multi-agent systems. In , Multi-Agent Systems : 18th European Conference, EUMAS 2021, virtual event, June 28–29, 2021, revised selected papers (S. 19–36). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Schäfer, B., der Aa, H., Leopold, H. und Stuckenschmidt, H. (2021). Sketch2BPMN: Automatic recognition of hand-drawn BPMN models. In , Advanced Information Systems Engineering : 33rd International Conference, CAiSE 2021, Melbourne, VIC, Australia, June 28 – July 2, 2021, proceedings (S. 344–360). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Schäfer, B. und Stuckenschmidt, H. (2021). DiagramNet: hand-drawn diagram recognition using visual arrow-relation detection. In , Document Analysis and Recognition – ICDAR 2021 : 16th International Conference, Lausanne, Switzerland, September 5–10, 2021, Proceedings, Part I (S. 614–630). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Sola, D. (2021). Towards a rule-based recommendation approach for business process modeling. In , Service-oriented computing – ICSOC 2020 Workshops : AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Dubai, United Arab Emirates, December 14–17, 2020 : proceedings (S. 25–31). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Sola, D., Meilicke, C., der Aa, H. und Stuckenschmidt, H. (2021). A rule-based recommendation approach for business process modeling. In , Advanced information systems engineering : 33rd International Conference, CAiSE 2021, Melbourne, VIC, Australia, June 28 – July 2, 2021, proceedings (S. 328–343). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
- Wilken, N. und Stuckenschmidt, H. (2021). Combining symbolic and statistical knowledge for goal recognition in smart home environments. In , 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (S. 26–31). , IEEE Computer Society: Piscataway, NJ.
- Wilken, N., Stuckenschmidt, H. und Bartelt, C. (2021). Combining symbolic and data-driven methods for goal recognition. In , 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (S. 428–429). , IEEE Computer Society: Piscataway, NJ.
2020
- Alhersh, T., Belhaouari, S. und Stuckenschmidt, H. (2020). Metrics performance analysis of optical flow. In , VISIGRAPP 2020 : proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Feb 27, 2020 – Feb 29, 2020, Valetta, Malta (S. 749–758). Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, SCITEPRESS – Science and Technology Publications: Setúbal.
- Burzlaff, F., Bongarth, B., Grottker, S., Hammen, J. und Bartelt, C. (2020). MergePoint: A graphical web-app for merging HTTP-endpoints and IoT-platform models. In , 53rd Hawaii International Conference on System Sciences, HICSS 2020 : Maui, Hawaii, USA, January 7–10, 2020 (S. 1–10). Proceedings of the 53rd Hawaii International Conference on System Sciences, ScolarSpace: Honolulu, HI.
- Debjit, P., Opitz, J., Becker, M., Kobbe, J., Hirst, G. und Frank, A. (2020). Argumentative relation classification with background knowledge. In , Computational models of argument : proceedings of COMMA 2020 (S. 319–330). Frontiers in Artificial Intelligence and Applications, IOS Press: Amsterdam.
- Fink, M., Meilicke, C. und Stuckenschmidt, H. (2020). Explaining differences between unaligned table snapshots. In , Advances in Database Technology – EDBT 2020, 23rd International Conference on Extending Database Technology, Copenhagen, Denmark, March 30 – April 02, proceedings (S. 133–144). , OpenProceedings.org: Copenhagen.
- Hulpus, I., Kobbe, J., Stuckenschmidt, H. und Hirst, G. (2020). Knowledge graphs meet moral values. In , Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics : Barcelona, Spain (Online), December 2020 (S. 71–80). , Association for Computational Linguistics: Stroudsburg, PA.
- Kobbe, J., Hulpus, I. und Stuckenschmidt, H. (2020). Unsupervised stance detection for arguments from consequences. In , EMNLP 2020 : proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 16th – 20th November 2020 (S. 50–60). , Association for Computational Linguistics: Online.
- Kobbe, J., Rehbein, I., Hulpus, I. und Stuckenschmidt, H. (2020). Exploring morality in argumentation. In , Proceedings of the 7th Workshop on Argument Mining : Barcelona, Spain (Online), December 13, 2020 (S. 30–40). , Association for Computational Linguistics, ACL: Stroudsburg, PA.
- Pernpeintner, M. (2020). Achieving emergent governance in competitive multi-agent systems. In , AAMAS '20 : Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, Auckland, Nea Zealand, May 2020 (S. 2204-2206). , ACM Digital Library: New York, NY.
- Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2020). Robust decision tree induction from unreliable data sources. In , STAIRS 2020 : Proceedings of the 9th European Starting AI Researchers' Symposium 2020 co-located with 24th European Conference on Artificial Intelligence (ECAI 2020) Santiago Compostela, Spain, August, 2020 (S. Paper 6, 1–8). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Schreckenberger, C., Glockner, T., Stuckenschmidt, H. und Bartelt, C. (2020). Restructuring of Hoeffding trees for Trapezoidal Data Streams. In , 20th IEEE International Conference on Data Mining Workshops : 17–20 November 2020, Virtual Conference : Proceedings (S. 416–423). , IEEE: Los Alamitos, CA [u. a.].
- Theil, C. K. und Stuckenschmidt, H. (2020). Predicting modality in financial dialogue. In , FNP-FNS 2020 : Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, December 2020, Barcelona, Spain (Online) (S. 226–234). ACL Anthology, Association for Computational Linguistics: Stroudsburg, PA.
- Štajner, S. und Hulpus, I. (2020). When shallow is good enough: Automatic assessment of conceptual text complexity using shallow semantic features. In , LREC 2020 Marseille : Twelfth International Conference on Language Resources and Evaluation : May 11–16, 2020, Palais du Pharo, Marseille, France : conference proceedings (S. 1414-1422). , European Language Resources Association, ELRA-ELDA: Paris.
- Štajner, S., Nisioi, S. und Hulpus, I. (2020). CoCo: A tool for automatically assessing conceptual complexity of texts. In , LREC 2020 Marseille : Twelfth International Conference on Language Resources and Evaluation : May 11–16, 2020, Palais du Pharo, Marseille, France : conference proceedings (S. 7179-7186). , European Language Resources Association: Paris.
2024
- Oesterle, M. (2024). Self-learning restriction-based governance of multi-agent systems. Dissertation. Mannheim.
2023
- Friede, D. (2023). Exploring discrete representations in stochastic computation graphs: Challenges, benefits, and novel strategies. Dissertation. Mannheim.
- Kobbe, J. (2023). Automatic generation of structured explanations for arguments from consequences. Dissertation. Mannheim.
- Schäfer, B. (2023). Recognizing hand-drawn diagrams in images. Dissertation. Mannheim.
- Sola, D. (2023). Recommending activities for business process models. Dissertation. Mannheim.
2022
- Alturki, S. (2022). Predicting students' academic achievement using methods of educational data mining. Dissertation. Mannheim.
- Theil, C. (2022). Uncertainty, risk, and financial disclosures : applications of natural language processing in behavioral economics. Dissertation. Mannheim.
2021
- Alhersh, T. (2021). From motion to human activity recognition. Dissertation. Mannheim.
- Burzlaff, F. (2021). Knowledge-driven architecture composition. Dissertation. Mannheim.
- Diete, A. (2021). Towards multimodal activity recognition in complex scenarios. Dissertation. Mannheim.
- Frendo, O. (2021). Improving smart charging for electric vehicle fleets by integrating battery and prediction models. Dissertation. Mannheim.
- Nolle, A. (2021). Federated knowledge base debugging in DL-Lite A. Dissertation. Mannheim.